System and method to operate a drone

ABSTRACT

A method for controlling a drone includes receiving a request for information about a spatial location, generating data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, and extracting and analyzing data to answer the request. The method can include extracting data points from the data types, obtaining labels from a user for one or more of the data points, predicting labels for unlabeled data points from a learning algorithm using the labels obtained from the user, determining the predicted labels are true labels for the unlabeled data points and combining the extracted data, the user labeled data points and the true labeled data points to answer the request for information. The learning algorithm may be active learning using a support vector machine.

BACKGROUND OF THE INVENTION

This disclosure relates generally to operating one or more drones andmore particularly to controlling one or more drones to respond to arequest for information.

Drones are small, lightweight aerial vehicles that are operated eitherautonomously by onboard computers or by a human operator via remotecontrol. For autonomous flight, drones contain a GPS device that guidesthem along a sequence of waypoints and enables them to return to theirlaunching point. Drones may carry small payloads, such as sensorpackages, cameras, or other types of small objects. The flight time of adrone is a function of its weight, battery capacity, and operatingenvironment, such as wind. Flight times generally range from 10-15minutes per battery for helicopter drones and 30-50 minutes forfixed-wing drones.

Drones are becoming increasingly popular in the hobbyist/consumermarket. Interest in the commercial use of drones is also increasing, asnew regulations in the U.S. are being crafted to enable commercial droneflights. Industries that drones will impact include agriculture,cinematography, mining, oil & gas, emergency response, and lawenforcement. These industries will need to control drones to respond torequests for information.

SUMMARY OF THE INVENTION

In one embodiment a method for controlling one or more drones to respondto a request for information is disclosed, that includes receiving arequest for information about a spatial location, generating a pluralityof data requests, configuring a flight plan for one or more drones overthe spatial location based on the plurality of data requests,controlling one or more drones to fly over the spatial locationaccording to the configured flight plan to obtain a plurality of datatypes from the spatial location based on the plurality of data requests,extracting data responsive to the plurality of data requests from theplurality of data types obtained by the one or more drones and analyzingthe responsive data to provide an answer to the request for information.

In one embodiment, the data types include one or more of data obtainedfrom an imaging system and data obtained from one or more sensors. Inone embodiment, configuring a flight plan for one or more dronesincludes selecting one or more drones based on matching dronecapabilities to one or more of the plurality of data requests. Inanother embodiment, controlling the one or more drones includes one ormore of uploading flight plans to the one or more drones, receivingreal-time telemetry from the drone, performing analytics on thereal-time telemetry to determine real-time flight conditions anddisplaying the real-time telemetry and real-time flight conditions on auser interface (UI) in a mobile application. One embodiment includesmanually controlling the flight path of the one or more drones from theUI. In one embodiment, the method includes storing the plurality of datatypes obtained by the one or more drones and creating location and timeindices for recall of the plurality of data types.

In another embodiment, the method for controlling one or more drones torespond to a request for information includes receiving a first requestfor information about a spatial location, parsing the first request intoa plurality of data requests, searching for existing sources for theplurality of data requests, determining that there are one or moreexisting sources for one or more of the plurality of data requests,analyzing the existing sources to obtain first data responsive to theplurality of data requests, determining that there are no existingsources for two or more of the plurality of data requests andidentifying the data requests with no existing source as missing datarequests. The method includes configuring a flight plan for one or moredrones over spatial location based on the missing data requests,controlling the one or more drones to fly over the spatial locationaccording to the configured flight plan to obtain a plurality of datatypes from the spatial location based on the missing data requests andextracting a plurality of data points responsive to the plurality ofdata requests from the plurality of data types obtained by the one ormore drones. The method also includes obtaining labels from a user forone or more of the plurality of data points, determining whether thereare unlabeled data points, predicting labels the for the unlabeled datapoints from a learning algorithm using the labels obtained from theuser, determining the predicted labels are true labels for the unlabeleddata points and combining the first data, the user labeled data pointsand the true labeled data points to provide an answer to the firstrequest for information. In one embodiment, the learning algorithm isactive learning using a support vector machine.

In another embodiment, the method further includes receiving a secondrequest for information about the spatial location, parsing the secondrequest into a plurality of second data requests, searching for existingsources for the plurality of second data requests, determining thatthere are one or more existing sources for one or more of the pluralityof second data requests, analyzing the existing sources to obtain seconddata responsive to the plurality of second data requests, determiningthat there are no existing sources for two or more of the plurality ofsecond data requests and identifying the data requests with no existingsource as missing data requests. The method also includes configuring aflight plan for one or more drones over the spatial location based onthe missing data requests, controlling the one or more drones to flyover the spatial location according to the configured flight plan toobtain a plurality of data types from the spatial location based on themissing data requests, extracting a plurality of data points responsiveto the plurality of data requests from the plurality of data typesobtained by the one or more drones. The method further includesdetermining that there are user labels and predicted true labels for allthe plurality of data points and combining the second data, the userlabeled data points and the predicted true labeled data points toprovide an answer to the request for information.

In one embodiment a non-transitory article of manufacture tangiblyembodying computer readable instructions, which when implemented, causea computer to perform the steps of a method for controlling one or moredrones to respond to a request for information, is disclosed thatincludes receiving a request for information about a spatial location,generating a plurality of data requests, configuring a flight plan forone or more drones over the spatial location based on the plurality ofdata requests, controlling one or more drones to fly over the spatiallocation according to the configured flight plan to obtain a pluralityof data types from the spatial location based on the plurality of datarequests, extracting data responsive to the plurality of data requestsfrom the plurality of data types obtained by the one or more drones andanalyzing the responsive data to provide an answer to the request forinformation.

In another embodiment, the non-transitory article of manufactureincludes computer readable instructions, which when implemented, cause acomputer to perform the steps of searching for existing sources for theplurality of data requests, determining that there are one or moreexisting sources for one or more of the plurality of data requests,analyzing the existing sources to obtain first data responsive to theplurality of data requests, determining that there are no existingsources for two or more of the plurality of data requests andidentifying the data requests with no existing source as missing datarequests. The computer readable instructions include configuring aflight plan for one or more drones over the spatial location based onthe missing data requests, controlling one or more drones to fly overthe spatial location according to the configured flight plan to obtain aplurality of data types from the spatial location based on the missingdata requests, extracting a plurality of data points responsive to theplurality of data requests from the plurality of data types obtained bythe one or more drones, obtaining labels from a user for one or more ofthe plurality of data points, determining whether there are unlabeleddata points and predicting labels for the unlabeled data points from alearning algorithm using the labels obtained from the user. The learningalgorithm may be in one embodiment active learning using a supportvector machine. The computer readable instructions include determiningthe predicted labels are true labels for the unlabeled data points andcombining the first data, the user labeled data points and the truelabeled data points to provide an answer to the first request forinformation.

In one embodiment a computer system for controlling one or more dronesto respond to a request for information is disclosed that includes oneor more computer processors, one or more non-transitorycomputer-readable storage media, program instructions, stored on the oneor more non-transitory computer-readable storage media, which whenimplemented by the one or more processors, cause the computer system toperform the steps of receiving a request for information about a spatiallocation, generating a plurality of data requests, configuring a flightplan for one or more drones over the spatial location based on theplurality of data requests, controlling one or more drones to fly overthe spatial location according to the configured flight plan to obtain aplurality of data types from the spatial location based on the pluralityof data requests, extracting data responsive to the plurality of datarequests from the plurality of data types obtained by the one or moredrones and analyzing the responsive data to provide an answer to therequest for information.

In another embodiment, the computer system also includes programinstructions which cause the computer system to perform the steps ofsearching for existing sources for the plurality of data requests,determining that there are one or more existing sources for one or moreof the plurality of data requests, analyzing the existing sources toobtain first data responsive to the plurality of data requests,determining that there are no existing sources for two or more of theplurality of data requests and identifying the data requests with noexisting source as missing data requests. The program instructionsinclude configuring a flight plan for one or more drones over thespatial location based on the missing data requests, controlling one ormore drones to fly over the spatial location according to the configuredflight plan to obtain a plurality of data types from the spatiallocation based on the missing data requests, extracting a plurality ofdata points responsive to the plurality of data requests from theplurality of data types obtained by the one or more drones, obtaininglabels from a user for one or more of the plurality of data points,determining whether there are unlabeled data points, predicting labelsfor the unlabeled data points from a learning algorithm using the labelsobtained from the user, determining the predicted labels are true labelsfor the unlabeled data points and combining the first data, the userlabeled data points and the true labeled data points to provide ananswer to the first request for information.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings, in which:

FIG. 1 is flow chart of one embodiment of a method for controlling oneor more drones to respond to a request for information.

FIG. 2 is flow chart of another embodiment of a method for controllingone or more drones to respond to a request for information.

FIG. 3A is a first part of a flow chart of one embodiment a method forcontrolling one or more drones to respond to a request for informationusing active learning.

FIG. 3B is a second part of a flow chart of one embodiment a method forcontrolling one or more drones to respond to a request for informationusing active learning.

FIG. 4A is a first part of flow chart of another embodiment a method forcontrolling one or more drones to respond to a request for informationusing active learning.

FIG. 4B is a second part of flow chart of another embodiment a methodfor controlling one or more drones to respond to a request forinformation using active learning.

FIG. 5 is a block diagram of an exemplary computing system suitable forimplementation of this invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

FIG. 1 is a flow chart of one embodiment of a method for controlling oneor more drones to respond to a request for information. The methodincludes at step 2 receiving a natural language request for informationabout a spatial location. Step 4 is parsing the natural language requestinto a plurality of data requests. One embodiment of the inventionincludes a method for making drone requests by using techniques fromnatural language processing to infer structured data from anunstructured specification. The data requests parsed from the naturallanguage request can include a set of data to be collected, a locationfrom which that data is collected, a pipeline of analytics to beperformed on that data, and a timeframe to collect the data. Forexample, one natural language request from a farmer may be “is therefrost on the south field today?” which may be combined with priorknowledge (e.g. the definition of “south field”) and domain-specificvocabulary (e.g. “frost”), to translate this into a request. The requestis parsed into include: location: south field, data: imagery, analytic:frost detection, when: today. In this way, the method maps from thetopic space of natural-language questions a requester might ask into themeasurement space of the data the drone can collect and analytics it canperform. In one embodiment, the natural language request is parsed tospecify a set of data to collect (e.g. images, video, temperature)within a specified geographic region (e.g. identified by GPScoordinates) by a certain time frame (e.g. by next week) with certainanalytics performed on the data (e.g. frost detection).

Step 6 is configuring a flight plan for one or more drones over thespatial location based on the plurality of data requests. In oneembodiment, terrain-aware, drone-aware and/or weather-aware flight plansare generated. This embodiment uses algorithms/heuristics thatincorporate awareness of ground terrain, energy costs of various flightoperations (increase/decrease ground elevation, change in direction,etc.), and current weather/wind conditions to generate optimal ornear-optimal flight plans with 100% ground coverage. The idea is tomaximize battery life by creating flight plans that take advantage ofthe underlying terrain model, paying attention to the energy-costfunctions for drone flight operations (ascend/descend, heading changes),and current wind conditions. For example, a flight plan over a hill ismore efficient when the drone makes one trip up the hill and thendescends the hill laterally to achieve full coverage, rather than makingmultiple trips up and down the hill to achieve coverage. Another exampleis to tailor the flight path to current wind conditions; e.g. flyinginto or perpendicular to the direction of the prevailing wind is moreefficient than flying against it. These heuristics enable accurateprediction of how long a particular task (e.g., mapping) will actuallytake the drone operator and required resources (especially, the numberof required batteries). Accuracy enables low-risk effective timemanagement. The approach is to create a grid of ground points. The UAVis required to reach (in succession) each of these points, at aspecified elevation over the ground. In doing so, the UAV will havecovered the specified terrain uniformly and completely (for the purposeof taking photographs, sensor readings, etc.) The invention optimizesthe order of each ground point such that the battery cost of the flightis minimized, extending flight times.

Step 8 is controlling one or more drones to fly over the spatiallocation according to the configured flight plan to obtain a pluralityof data types from the spatial location based on the plurality of datarequests. Step 10 is extracting data responsive to the plurality of datarequests from the plurality of data types obtained by the one or moredrones. Step 14 is analyzing the responsive data to provide an answer tothe natural language request for information. In one embodiment, theinvention uses a series of analytics to clean, process, and analyze datacollected by the drone. These analytics may operate in a pipelinefashion, e.g. using a technology such as UIMA, in order to satisfy datadependencies. For example, a sample pipeline for a series of imagescollected by a drone might include selecting a subset of images based ontheir clarity (i.e. discard blurry images), stitching the images to forman orthorectified mosaic and overlaying sensor data collected by thedrone to create a visual heat map.

Analytics may include tracking the operating characteristics of dronesand alerting operators when maintenance may be required. Usingstatistics on drone usage, such as usage-hours, accelerometer data, andgyroscope data, the user can perform predictive maintenance tasks basedon analytics that consider drone failure rates across fleets of drones.

Step 16 is storing the data types with location and time indices. In oneembodiment, the data types includes one or more of data obtained from animaging system and data obtained from one or more sensors on the drones.One embodiment includes storing heterogeneous data captured by the droneand creating spatio-temporal indices so the data can be recalled basedon location and time. Spatiotemporal indices help the drone operatoridentify what spatial or temporal coverage gaps exist in the fulfillmentof a request. Once the set of data is complete, the spatial index helpsensure that only data is shared back to the requestor that the requestorshould be able to see. For example, a farmer should not see data from anadjacent farm, even if the UAV has incidentally captured data from thatfarm. The spatial index can identify what data (e.g., video footage ormulti-spectral imagery) applies to a specific field.

As shown in FIG. 2, in one embodiment the method may also include step20 of configuring a flight plan for one or more drones by selecting oneor more drones based on matching drone capabilities to one or more ofthe plurality of data requests. A drone registry can track the hardwarecapabilities of the drones in the fleet, e.g. drone type (quad, octo,fixed wing), flight time, payload capacity, propeller requirements, aswell as the sensor payloads with which they are equipped. For example,drones can have one or more of an optical camera, an IR camera, an NIRcamera, a temperature sensor and an optical flow sensor. One example ofmatching the capabilities of the drone to the data request is matching adrone that contains an NIR camera to a data request of “create an NDVImap of my farm.” An example of a request that does not match therequirements is a drone that only contains an optical camera and therequest is to map the temperature of this field.

In another embodiment, controlling the one or more drones includes step22 of uploading flight plans to the one or more drones from a userinterface (UI), step 24 of receiving real-time telemetry from the dronesand displaying the telemetry on the UI, step 26 of performing analyticson the real-time telemetry to determine real-time flight conditions anddisplaying the flight conditions on the UI in a mobile application. Oneembodiment includes step 28 of manually controlling the flight path ofthe one or more drones from the UI.

In another embodiment, a drone request is generated automatically aspart of an active learning system responsible for maintaining a Deep Q&Ainterface to users. In this embodiment, the Q&A interface allowsindividuals to formulate natural language questions about theenvironment in certain spatial locations, including aspects such aslocal weather, traffic conditions, vegetation properties, structures andtheir status and persons moving through an environment. The system thenperforms a search of sensor data to determine if the question can beanswered with a sufficient degree of confidence (C) to exceed athreshold (T) and trigger a response. If C<T, a drone active learningcomponent is activated, which automatically configures a drone requestto gather additional information for answering the question. Activelearning, as is known in the art, typically involves querying experthumans pertaining to questions on a particular subject. The use ofdrones to gather information for a similar active learning solution isused in this disclosure to construct a Q&A system around environmentaland geographical topics.

In this embodiment, the human annotator is a drone, and the labels thedrone provides are extracted from observations and data gathered by thedrone. In one example, a series of steps implementing this embodimentmay include:

Farmer logs into a Deep Q&A interface for his thousand acre farm.

Farmer poses a natural language question: “What is the risk of a viralinfection taking hold due to the recent drought in the upper north fieldof sweet potatoes?”

Deep Q&A system parses this question, and an active learning componentis activated by an identified need to survey the crop and assessvariance in crop stress observed by leaf desiccation patterns based onan agriculture journal article correlated this variance with viralinfection taking hold and spreading from most stressed plants.

Deep Q&A configures a drone to perform a hyperspectral image survey ofthe field identified.

Drone gathers data from geographical location and relays it back to theactive learning component.

Active learning component labels the data by for example measuring meanand variance, for training inclusion in a support vector machinetraining and categorizes risk of infection.

Active learning is a special case of semi-supervised machine learning inwhich a learning algorithm is able to interactively query the user (orsome other information source) to obtain the desired outputs at new datapoints. In statistics literature it is sometimes also called optimalexperimental design. There are situations in which unlabeled data isabundant but manually labeling is expensive. In such a scenario,learning algorithms can actively query the user/teacher for labels. Thistype of iterative supervised learning is called active learning. Sincethe learner chooses the examples, the number of examples to learn aconcept can often be much lower than the number required in normalsupervised learning. With this approach, there is a risk that thealgorithm be overwhelmed by uninformative examples. Recent developmentsare dedicated to hybrid active learning and active learning in asingle-pass (on-line) context, combining concepts from the field ofMachine Learning (e.g., conflict and ignorance) with adaptive,incremental learning policies in the field of Online machine learning.

Some active learning algorithms are built upon Support vector machines(SVMs) and exploit the structure of the SVM to determine which datapoints to label. Such methods usually calculate the margin, W, of eachunlabeled datum in T_{U,i} and treat W as an n-dimensional distance fromthat datum to the separating hyperplane.

Minimum Marginal Hyperplane methods assume that the data with thesmallest W are those that the SVM is most uncertain about and thereforeshould be placed in T_{C,i} to be labeled. Other similar methods, suchas Maximum Marginal Hyperplane, choose data with the largest W. Tradeoffmethods choose a mix of the smallest and largest Ws.

In example above of the farmer, the system is seeking to predictvariance in drought stress in a crop by learning drone hyperspectralimaging features, collected automatically by drone, and training asupport vector machine on these features, given labels derived fromanother source, for example, a subsequent assessment by a farmer takingsamples in a grid pattern throughout his field\. In active learning,labels are costly to collect, and the goal is to predict labels fromless costly measures, in this case from automatic drone based images.

In this embodiment of the invention, the method answers farmer'squestion and learns the predictors of the labels gathered via the dronefor future inference in the absence of drone observation. Thisembodiment incorporates the learned predictors of the labels that thesystem has generated from the drone hyperspectral imaging data and thefarmer's labels into the Deep Q&A system itself. Once incorporated,whenever a similar query is made, either directly, or indirectly asevidence is gathered for a secondary assessment, about the variance ofcrop stress due to drought, the drone is automatically deployed andcollects data. These data are then automatically assessed by theactively learned classifiers, and the predicted labels are immediately,and cheaply, incorporated into the formulation of an answer to the DeepQ&A query.

FIGS. 3A and 3B is a flow chart of an embodiment of the method forcontrolling a drone to respond to a request for information using alearning technique. As shown in FIG. 3A, step 30 is receiving a firstnatural language request for information about a spatial location. Step32 is parsing the first natural language request into a plurality ofdata requests. Step 34 is searching for existing sources for theplurality of data requests. Step 36 is determining whether there are oneor more existing sources for one or more of the plurality of datarequests. If the result is Yes in step 36, the method moves to step 38.Step 38 is analyzing the existing sources to obtain first dataresponsive to the plurality of data requests. Step 40 is determiningwhether there are no existing sources for two or more of the pluralityof data requests. If the result is No to step 40 then the systemprovides an answer to the request for information based on the analysisof the existing sources. If the answer is Yes to step 40 data requestswith no existing source are identified as missing data requests. Themethod then moves to step 44 when the result of step 36 is No or theresult of step 40 is Yes. Step 44 is configuring a flight plan for oneor more drones over spatial location based on the missing data requests.

As shown in FIG. 3B, step 46 is controlling the one or more drones tofly over the spatial location according to the configured flight plan.Step 48 is obtaining a plurality of data types from the spatial locationbased on the missing data requests. Step 50 is extracting a plurality ofdata points responsive to the plurality of data requests from theplurality of data types obtained by the one or more drones. Step 52 isobtaining labels from a user for one or more of the plurality of datapoints. Step 54 is determining whether there are unlabeled data points.If there are no unlabeled data points the method moves to step 60. Ifthere are unlabelled data points, step 56 is predicting labels for theunlabeled data points from a learning algorithm using the labelsobtained from the user. Step 58 is determining whether the predictedlabels are true labels for the unlabeled data points. Predicted labelsthat are determined not to be true labels are discarded in step 59. Ifpredicted labels are determined to be true labels, the method moves tostep 60. Step 60 is combining the first data, the user labeled datapoints and the true labeled data points to provide in step 62 an answerto the first natural language request for information. In oneembodiment, the learning algorithm is active learning using a supportvector machine.

As shown in FIG. 4A, in another embodiment, the method further includesat step 64 receiving a second natural language request for informationabout the spatial location and parsing the second natural languagerequest into a plurality of second data requests at step 66. Step 68 issearching for existing sources for the plurality of second datarequests. Step 70 is determining whether there are one or more existingsources for one or more of the plurality of second data requests. Ifthere are no existing sources, the method moves to step 78. If there areexisting sources, the method moves to step 72. Step 72 is analyzing theexisting sources to obtain second data responsive to the plurality ofsecond data requests. Step 74 is determining whether there are noexisting sources for two or more of the plurality of second datarequests and identifying the data requests with no existing source asmissing data requests. If the are no missing data requests, the methodprovides an answer based on the analysis of the existing sources at step76. If there are missing data requests, the method moves to step 78which is configuring a flight plan for one or more drones over thespatial location based on the missing data requests. Step 80 iscontrolling the one or more drones to fly over the spatial locationaccording to the configured flight plan to obtain a plurality of datatypes from the spatial location based on the missing data requests atstep 82.

As shown in FIG. 4B, step 84 is extracting a plurality of data pointsresponsive to the plurality of data requests from the plurality of datatypes obtained by the one or more drones. Thereafter, the method movesto determining whether there are user labels or a predicted true labelsfor each of the plurality of data points. If there are data pointswithout a user label or a true predicted label then the method returnsto step 52. If the result of step 8 is Yes, the method moves to step 88which is combining the second data, the user labeled data points and thepredicted true labeled data points to provide an answer to the secondnatural language request for information at step 90.

The methods described herein can be implemented as part of a system forthe management of a fleet Unmanned Aerial Vehicles (UAVs, or “drones”).The system can a set of tasks requested by one or more task requesters,map of each task to one or more drones, based on the requirements of thetask and the capabilities of the drones, compute terrain-aware droneflight plans, store the multi-modal data captured by the drone(including flight telemetry logs, visual imagery, IR imagery, and sensordata), provide temporal and geospatial indexing of the drone data foruse in analytics applications and provide notifications andvisualization of analytics results. One or more commercial,off-the-shelf drones can used, for example, drones manufactured by3DRobotics (Iris, Iris+, X8, X8-M) and DJI (S1000).

In one embodiment, the method can detect gaps in a spatiotemporal dataset by identifying regions in which either no drone data has beencollected, or in which the age of the last data collected by a drone ina specified region exceeds some threshold. By identifying these gaps,the method can automatically (or with manual approval) generate requestsfor this data. E.g., for a given field, the method can detect that thelast images collected were ten days ago and that no temperature data hasbeen collected, triggering a request for imagery and temperature data.

FIG. 5 illustrates a schematic of an example computer or processingsystem that may implement the method for controlling one or more dronesto respond to a request for information in one embodiment of the presentdisclosure. The computer system is only one example of a suitableprocessing system and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the methodologydescribed herein. The processing system shown may be operational withnumerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with the processing system shown in FIG. 5 may include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 100, a system memory 106, anda bus 104 that couples various system components including system memory106 to processor 100. The processor 100 may include a program module 102that performs the methods described herein. The module 102 may beprogrammed into the integrated circuits of the processor 100, or loadedfrom memory 106, storage device 108, or network 114 or combinationsthereof.

Bus 104 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 106 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 108 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 104 by one or more data media interfaces.

Computer system may also communicate with one or more external devices116 such as a keyboard, a pointing device, a display 118, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 110.

Still yet, computer system can communicate with one or more networks 114such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 112. Asdepicted, network adapter 112 communicates with the other components ofcomputer system via bus 104. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include anon-transitory computer readable storage medium (or media) havingcomputer readable program instructions thereon for causing a processorto carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

In addition, while preferred embodiments of the present invention havebeen described using specific terms, such description is forillustrative purposes only, and it is to be understood that changes andvariations may be made without departing from the spirit or scope of thefollowing claims.

What is claimed is:
 1. A computer implemented method for controlling oneor more drones to respond to a request for information, comprising;receiving a request for information about a spatial location; generatinga plurality of data requests, each data request of the plurality of datarequests corresponding to a portion of data necessary to answer therequest; configuring a flight plan for one or more drones over thespatial location based on the plurality of data requests; controllingone or more drones to fly over the spatial location according to theconfigured flight plan to obtain a plurality of data types from thespatial location based on the plurality of data requests, each data typeproviding a corresponding portion of the data necessary to answer therequest; extracting data responsive to the plurality of data requestsfrom the plurality of data types obtained by the one or more drones; andanalyzing the responsive data to provide an answer to the request forinformation.
 2. The computer implemented method of claim 1, wherein thedata types include one or more of data obtained from an imaging systemand data obtained from one or more sensors and wherein the plurality ofdata requests include one or more of a set of data to be collected, alocation from which the data set is to be collected, analytics to beperformed on the data set a timeframe to collect the data set.
 3. Thecomputer implemented method of claim 1, further comprising selecting oneor more drones based on matching drone capabilities to one or more ofthe plurality of data requests and uploading flight plans to the one ormore drones, receiving real-time telemetry from the drone and performinganalytics on the real-time telemetry to determine real-time flightconditions.
 4. The computer implemented method of claim 3, furthercomprising displaying the real-time telemetry and real-time flightconditions on a user interface (UI) in a mobile application and manuallycontrolling the flight path of the one or more drones from the UI. 5.The computer implemented method of claim 1, further comprising:extracting a plurality of data points responsive to the plurality ofdata requests from the plurality of data types obtained by the one ormore drones; obtaining labels for one or more of the plurality of datapoints; and combining the extracted data and the labeled data points toprovide an answer to the request for information.
 6. The computerimplemented method of claim 5, further comprising: obtaining labels froma user for one or more of the plurality of data points; predictinglabels for unlabeled data points from a learning algorithm using thelabels obtained from the user; determining the predicted labels are truelabels for the unlabeled data points; and combining the extracted data,the user labeled data points and the true labeled data points to providean answer to the request for information.
 7. The computer implementedmethod of claim 1, further comprising; searching for existing sourcesfor the plurality of data requests; determining that there are one ormore existing sources for one or more of the plurality of data requests;analyzing the existing sources to obtain first data responsive to theplurality of data requests; determining that there are no existingsources for two or more of the plurality of data requests andidentifying the data requests with no existing source as missing datarequests; wherein configuring a flight plan comprises configuring aflight plan for one or more drones over the spatial location based onthe missing data requests; wherein controlling one or more dronescomprises controlling one or more drones to fly over the spatiallocation according to the configured flight plan to obtain a pluralityof data types from the spatial location based on the missing datarequests; wherein extracting data comprises extracting a plurality ofdata points responsive to the plurality of data requests from theplurality of data types obtained by the one or more drones obtaininglabels from a user for one or more of the plurality of data points;determining whether there are unlabeled data points; predicting labelsfor the unlabeled data points from a learning algorithm using the labelsobtained from the user; determining the predicted labels are true labelsfor the unlabeled data points; and wherein analyzing responsive datacomprises combining the first data, the user labeled data points and thetrue labeled data points to provide an answer to the request forinformation.
 8. The computer implemented method of claim 7, wherein thelearning algorithm comprises active learning using a support vectormachine.
 9. The computer implemented method of claim 8, furthercomprising: receiving a second request for information about the spatiallocation; parsing the second request into a plurality of second datarequests; searching for existing sources for the plurality of seconddata requests; determining that there are one or more existing sourcesfor one or more of the plurality of second data requests; analyzing theexisting sources to obtain second data responsive to the plurality ofsecond data requests; determining that there are no existing sources fortwo or more of the plurality of second data requests and identifying thedata requests with no existing source as missing data requests;configuring a flight plan for one or more drones over the spatiallocation based on the missing data requests; controlling one or moredrones to fly over the spatial location according to the configuredflight plan to obtain a plurality of data types from the spatiallocation based on the missing data requests; extracting a plurality ofdata points responsive to the plurality of data requests from theplurality of data types obtained by the one or more drones; determiningthat there is a user label or a predicted true label for each of theplurality of data points; and combining the second data, the userlabeled data points and the predicted true labeled data points toprovide an answer to the second request for information.
 10. Anon-transitory article of manufacture tangibly embodying computerreadable instructions, which when implemented, cause a computer toperform the steps of a method for controlling one or more drones torespond to a request for information, comprising; receiving a requestfor information about a spatial location; generating a plurality of datarequests, each data request of the plurality of data requestscorresponding to a portion of data necessary to answer the request;configuring a flight plan for one or more drones over the spatiallocation based on the plurality of data requests; controlling one ormore drones to fly over the spatial location according to the configuredflight plan to obtain a plurality of data types from the spatiallocation based on the plurality of data requests, each data typeproviding a corresponding portion of the data necessary to answer therequest; extracting data responsive to the plurality of data requestsfrom the plurality of data types obtained by the one or more drones; andanalyzing the responsive data to provide an answer to the request forinformation.
 11. The non-transitory article of manufacture of claim 10,further comprising computer readable instructions, which whenimplemented, cause a computer to perform the steps of: extracting aplurality of data points responsive to the plurality of data requestsfrom the plurality of data types obtained by the one or more drones,wherein the data types include one or more of data obtained from animaging system and data obtained from one or more sensors and whereinthe plurality of data requests include one or more of a set of data tobe collected, a location from which the data set is to be collected,analytics to be performed on the data set a timeframe to collect thedata set; obtaining labels for one or more of the plurality of datapoints; and combining the extracted data and the labeled data points toprovide an answer to the request for information.
 12. The non-transitoryarticle of manufacture of claim 11, further comprising computer readableinstructions, which when implemented, cause a computer to perform thesteps of: obtaining labels from a user for one or more of the pluralityof data points; predicting labels for unlabeled data points from alearning algorithm using the labels obtained from the user; determiningthe predicted labels are true labels for the unlabeled data points; andcombining the extracted data, the user labeled data points and the truelabeled data points to provide an answer to the request for information13. The non-transitory article of manufacture of claim 10, furthercomprising computer readable instructions, which when implemented, causea computer to perform the steps of: searching for existing sources forthe plurality of data requests; determining that there are one or moreexisting sources for one or more of the plurality of data requests;analyzing the existing sources to obtain first data responsive to theplurality of data requests; determining that there are no existingsources for two or more of the plurality of data requests andidentifying the data requests with no existing source as missing datarequests; wherein configuring a flight plan comprises configuring aflight plan for one or more drones over the spatial location based onthe missing data requests; wherein controlling one or more dronescomprises controlling one or more drones to fly over the spatiallocation according to the configured flight plan to obtain a pluralityof data types from the spatial location based on the missing datarequests; wherein extracting data comprises extracting a plurality ofdata points responsive to the plurality of data requests from theplurality of data types obtained by the one or more drones obtaininglabels from a user for one or more of the plurality of data points;determining whether there are unlabeled data points; predicting labelsfor the unlabeled data points from a learning algorithm using the labelsobtained from the user, the learning algorithm comprising activelearning using a support vector machine; determining the predictedlabels are true labels for the unlabeled data points; and whereinanalyzing the responsive data comprises combining the first data, theuser labeled data points and the true labeled data points to provide ananswer to the request for information.
 14. The non-transitory article ofmanufacture of claim 13, further comprising computer readableinstructions, which when implemented, cause a computer to perform thesteps of: receiving a second request for information about the spatiallocation; parsing the second request into a plurality of second datarequests; searching for existing sources for the plurality of seconddata requests; determining that there are one or more existing sourcesfor one or more of the plurality of second data requests; analyzing theexisting sources to obtain second data responsive to the plurality ofsecond data requests; determining that there are no existing sources fortwo or more of the plurality of second data requests and identifying thedata requests with no existing source as missing data requests;configuring a flight plan for one or more drones over the spatiallocation based on the missing data requests; controlling one or moredrones to fly over the spatial location according to the configuredflight plan to obtain a plurality of data types from the spatiallocation based on the missing data requests; extracting a plurality ofdata points responsive to the plurality of data requests from theplurality of data types obtained by the one or more drones; determiningthat there is a user label or a predicted true labels for each of theplurality of data points; and combining the existing source obtaineddata points, the user labeled data points and the predicted true labeleddata points to provide an answer to the second request for information.15. A computer system for controlling one or more drones to respond to arequest for information, comprising: one or more computer processors;one or more non-transitory computer-readable storage media; programinstructions, stored on the one or more non-transitory computer-readablestorage media, which when implemented by the one or more processors,cause the computer system to perform the steps of: receiving a requestfor information about a spatial location; generating a plurality of datarequests, each data request of the plurality of data requestscorresponding to a portion of data necessary to answer the request;configuring a flight plan for one or more drones over the spatiallocation based on the plurality of data requests; controlling one ormore drones to fly over the spatial location according to the configuredflight plan to obtain a plurality of data types from the spatiallocation based on the plurality of data requests, each data typeproviding a corresponding portion of the data necessary to answer therequest; extracting data responsive to the plurality of data requestsfrom the plurality of data types obtained by the one or more drones; andanalyzing the responsive data to provide an answer to the request forinformation.
 16. The computer system of claim 15, further comprisingprogram instructions which cause the computer system to perform thesteps of: uploading flight plans to the one or more drones; receivingreal-time telemetry from the drone; performing analytics on thereal-time telemetry to determine real-time flight conditions, anddisplaying the real-time telemetry and real-time flight conditions on auser interface (UI) in a mobile application.
 17. The computer system ofclaim 15, further comprising program instructions which cause thecomputer system to perform the steps of: extracting a plurality of datapoints responsive to the plurality of data requests from the pluralityof data types obtained by the one or more drones, wherein the data typesinclude one or more of data obtained from an imaging system and dataobtained from one or more sensors and wherein the plurality of datarequests include one or more of a set of data to be collected, alocation from which the data set is to be collected, analytics to beperformed on the data set a timeframe to collect the data set; obtaininglabels for one or more of the plurality of data points; and combiningthe extracted data and the labeled data points to provide an answer tothe request for information.
 18. The computer system of claim 17,further comprising program instructions which cause the computer systemto perform the steps of: obtaining labels from a user for one or more ofthe plurality of data points; predicting labels for unlabeled datapoints from a learning algorithm using the labels obtained from theuser; determining the predicted labels are true labels for the unlabeleddata points; and combining the extracted data, the user labeled datapoints and the true labeled data points to provide an answer to therequest for information.
 19. The computer system of claim 15, furthercomprising program instructions which cause the computer system toperform the steps of: searching for existing sources for the pluralityof data requests; determining that there are one or more existingsources for one or more of the plurality of data requests; analyzing theexisting sources to obtain first data responsive to the plurality ofdata requests; determining that there are no existing sources for two ormore of the plurality of data requests and identifying the data requestswith no existing source as missing data requests; wherein configuring aflight plan comprises configuring a flight plan for one or more dronesover the spatial location based on the missing data requests; whereincontrolling one or more drones comprises controlling one or more dronesto fly over the spatial location according to the configured flight planto obtain a plurality of data types from the spatial location based onthe missing data requests; wherein extracting data comprises extractinga plurality of data points responsive to the plurality of data requestsfrom the plurality of data types obtained by the one or more drones;obtaining labels from a user for one or more of the plurality of datapoints; determining whether there are unlabeled data points; predictinglabels for the unlabeled data points from a learning algorithm using thelabels obtained from the user, the learning algorithm comprising activelearning using a support vector machine; determining the predictedlabels are true labels for the unlabeled data points; and whereinanalyzing the responsive data comprises combining the first data, theuser labeled data points and the true labeled data points to provide ananswer to the request for information.
 20. The computer system of claim19, further comprising program instructions which cause the computersystem to perform the steps of: receiving a second request forinformation about the spatial location; parsing the second request intoa plurality of second data requests; searching for existing sources forthe plurality of second data requests; determining that there are one ormore existing sources for one or more of the plurality of second datarequests; analyzing the existing sources to obtain second dataresponsive to the plurality of second data requests; determining thatthere are no existing sources for two or more of the plurality of seconddata requests and identifying the data requests with no existing sourceas missing data requests; configuring a flight plan for one or moredrones over the spatial location based on the missing data requests;controlling one or more drones to fly over the spatial locationaccording to the configured flight plan to obtain a plurality of datatypes from the spatial location based on the missing data requests;extracting a plurality of data points responsive to the plurality ofdata requests from the plurality of data types obtained by the one ormore drones; determining that there is a user labels or predicted truelabel for each of the plurality of data points; and combining theexisting source obtained data points, the user labeled data points andthe predicted true labeled data points to provide an answer to thesecond request for information.